<![CDATA[Cloud services, edge devices, and organizational information systems expose security teams to a shared problem: a locally accurate detector can still produce a poor operational decision when its recommendations ignore service dependencies, tenant boundaries, privacy constraints, or the consequences of interruption. This paper develops a design-science framework for coordinating these concerns within a single decision process. A purposive corpus of 55 supplied publications informs the requirements; it is treated as a bounded design resource rather than a systematic or exhaustive evidence base. The proposed architecture connects tenant-local telemetry, context-sensitive threat estimation, privacy-preserving collaborative learning, dependency-aware response selection, and verifiable governance records. Its central contribution is a constrained decision formulation that minimizes expected attack loss and response-induced disruption while enforcing explicit limits on privacy expenditure, latency, and service availability. Analytical examination establishes three conditional properties: independent event thresholds can violate shared service constraints, selective abstention can reduce decision loss when review is sufficiently informative, and federated participation does not by itself establish privacy. A transparent worked example demonstrates how a capacity constraint changes which alerts receive an automated response; the example is illustrative and does not represent measured performance. A prospective evaluation protocol specifies chronological and tenant-separated testing, rare-attack assessment, poisoning experiments, privacy accounting, recovery analysis, and paired comparisons against operational baselines. No experiments or production deployments are reported. The framework provides an implementable research specification and testable propositions for examining whether coordinated cyber defense improves operational outcomes beyond predictive accuracy alone.]]>
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